code: def __init__(
      self,
      graph,
      rep_size=128,
      epoch=120,
      learning_rate=0.003,
      weight_decay=1.0,
  ):
      self.g = graph
  
      self.node_size = graph.G.number_of_nodes()
      self.rep_size = rep_size
      self.max_iter = epoch
      self.lr = learning_rate
      self.lamb = weight_decay
      self.sess = tf.Session()
      self.adj_mat = self.getAdj()
      self.vectors = {}
  
      self.embeddings = self.get_train()
  
      look_back = self.g.look_back_list
  
      for i, embedding in enumerate(self.embeddings):
          self.vectors[look_back[i]] = embedding
  
file_path: OpenNE/src/openne/gf.py
imports: from __future__ import print_function
  import numpy
  import tensorflow
  import networkx
label_desc: Use image node_id 1 to create a new GraphFactorization object from inherited base classes: object with example: obj = GraphFactorization(graph, rep_size, epoch, learning_rate, weight_decay)